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Precisely controlling macromolecular stereochemistry and sequences is a powerful strategy for manipulating polymer properties. Controlled synthetic routes to prepare degradable polyester, polycarbonate, and polyether are of recent interest due to the need for sustainable materials as alternatives to petrochemical-based polyolefins. Enantioselective ring-opening polymerization and ring-opening copolymerization of racemic monomers offer access to stereoregular polymers, specifically enantiopure polymers that form stereocomplexes with improved physicochemical and mechanical properties. Here, we highlight the state-of-the-art of this polymerization chemistry that can produce microstructure-defined polymers. In particular, the structures and performances of various homogeneous enantioselective catalysts are presented. Trends and future challenges of such chemistry are discussed.

Stereoselective ring-opening polymerization catalysts are used to produce degradable stereoregular poly(lactic acids) with thermal and mechanical properties that are superior to those of atactic polymers. However, the process of discovering highly stereoselective catalysts is still largely empirical. We aim to develop an integrated computational and experimental framework for efficient, predictive catalyst selection and optimization. As a proof of principle, we have developed a Bayesian optimization workflow on a subset of literature results for stereoselective lactide ring-opening polymerization, and using the algorithm, we identify multiple new Al complexes that catalyze either isoselective or heteroselective polymerization. In addition, feature attribution analysis uncovers mechanistically meaningful ligand descriptors, such as percent buried volume (%V_bur) and the highest occupied molecular orbital energy (E_HOMO), that can access quantitative and predictive models for catalyst development.

Dimensionless numbers and scaling laws provide elegant insights into the characteristic properties of physical systems. Classical dimensional analysis and similitude theory fail to identify a set of unique dimensionless numbers for a highly multi-variable system with incomplete governing equations. This paper introduces a mechanistic data-driven approach that embeds the principle of dimensional invariance into a two-level machine learning scheme to automatically discover dominant dimensionless numbers and governing laws (including scaling laws and differential equations) from scarce measurement data. The proposed methodology, called dimensionless learning, is a physics-based dimension reduction technique. It can reduce high-dimensional parameter spaces to descriptions involving only a few physically interpretable dimensionless parameters, greatly simplifying complex process design and system optimization. We demonstrate the algorithm by solving several challenging engineering problems with noisy experimental measurements (not synthetic data) collected from the literature. Examples include turbulent Rayleigh-Bénard convection, vapor depression dynamics in laser melting of metals, and porosity formation in 3D printing. Lastly, we show that the proposed approach can identify dimensionally homogeneous differential equations with dimensionless number(s) by leveraging sparsity-promoting techniques.

Metal additive manufacturing provides remarkable flexibility in geometry and component design, but localized heating/cooling heterogeneity leads to spatial variations of as-built mechanical properties, significantly complicating the materials design process. To this end, we develop a mechanistic data-driven framework integrating wavelet transforms and convolutional neural networks to predict location-dependent mechanical properties over fabricated parts based on process-induced temperature sequences, i.e., thermal histories. The framework enables multiresolution analysis and importance analysis to reveal dominant mechanistic features underlying the additive manufacturing process, such as critical temperature ranges and fundamental thermal frequencies. We systematically compare the developed approach with other machine learning methods. The results demonstrate that the developed approach achieves reasonably good predictive capability using a small amount of noisy experimental data. It provides a concrete foundation for a revolutionary methodology that predicts spatial and temporal evolution of mechanical properties leveraging domain-specific knowledge and cutting-edge machine and deep learning technologies.

Transforming renewable resources into functional and degradable polymers is driven by the ever‐increasing demand to replace unsustainable polyolefins. However, the utility of many degradable homopolymers remains limited due to their inferior properties compared to commodity polyolefins. Therefore, the synthesis of sequence‐defined copolymers from one‐pot monomer mixtures is not only conceptually appealing in chemistry, but also economically attractive by maximizing materials usage and improving polymers’ performances. Among many polymerization strategies, ring‐opening (co)polymerization of cyclic monomers enables efficient access to degradable polymers with high control on molecular weights and molecular weight distributions. Herein, we highlight recent advances in achieving one‐pot, sequence‐controlled polymerizations of cyclic monomer mixtures using a single catalytic system that combines multiple catalytic cycles. The scopes of cyclic monomers, catalysts, and polymerization mechanisms are presented for this type of sequence‐controlled ring‐opening copolymerization.